Abstract
In this paper, using machine learning, a dual-band F-shaped RFID antenna is designed to operate in 867MHz UHF and 2.45GHz WLAN bands. The study's dataset was consisting of a total of 625 samples, and this was received from the simulation software as a consequence of the parametric analysis of the design parameters for the antenna. The success of the algorithms was compared after six of the most popular machine learning algorithms were applied to the same parameters. The Random Forest algorithm, which has a 99.96% for R2 score and a mean squared error value of 0.0004, has been used to predict the input port scattering parameter. With the best results obtained from this technique, the antenna operating at the desired frequencies was designed.
Keywords
Dual Band Antenna Design Machine Learning Radio Frequency Identification Regression Scattering Parameters
References
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Abstract
References
- [1] C. Maeurer, P. Futter, and G. Gampala, “Antenna Design Exploration and Optimization using Machine Learning,” in 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020, pp. 1–5. doi: 10.23919/EuCAP48036.2020.9135530.
- [2] A. Desai, I. Akdag, M. Palandoken, C. D. Bui, J. Kulkarni, and T. K. Nguyen, “Wide Slot Circularly Polarized Conductive Oxide-based Transparent Antenna Design for ISM Band RFID Applications,” in 2021 International Conference on Advanced Technologies for Communications (ATC), 2021, pp. 217–221. doi: 10.1109/ATC52653.2021.9598335.
- [3] M. I. M. Ghazali, S. Karuppuswami, and M. H. Jamaluddin, “Machine Learning based Design Optimization of a GPS Antenna on a flexible substrate,” in 2021 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE), 2021, pp. 1–3. doi: 10.1109/APACE53143.2021.9760562.
- [4] I. Akdag, C. Gocen, M. Palandoken, and A. Kaya, “A novel circularly polarized reader antenna design for UHF RFID applications,” Wireless Networks, vol. 28, no. 6, pp. 2625–2636, 2022, doi: 10.1007/s11276-022-02998-8.
- [5] S. Sagiroglu and K. Güney, “Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks,” Microw Opt Technol Lett, vol. 14, no. 2, pp. 89–93, 1997, doi: https://doi.org/10.1002/(SICI)1098-2760(19970205)14:2.
- [6] C. R. M. Silva and S. R. Martins, “An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Optimization Using a Machine Learning Technique,” Microw Opt Technol Lett, vol. 55, no. 8, pp. 1864–1868, 2013, doi: https://doi.org/10.1002/mop.27692.
- [7] Z. Zheng, X. Chen, and K. Huang, “Application of support vector machines to the antenna design,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 21, no. 1, pp. 85–90, 2011, doi: https://doi.org/10.1002/mmce.20491.
- [8] J.-H. Lu and S.-F. Wang, “Planar Broadband Circularly Polarized Antenna With Square Slot for UHF RFID Reader,” IEEE Trans Antennas Propag, vol. 61, no. 1, pp. 45–53, 2013, doi: 10.1109/TAP.2012.2220103.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 26, 2022 |
| Submission Date | October 31, 2022 |
| Published in Issue | Year 2022 Volume: 2 Issue: 2 |
All articles published by JAIDA are licensed under a Creative Commons Attribution 4.0 International License.
